refactor : 清理工厂和配置系统中的死代码与冗余抽象

- 删除 Registry 中未使用的 category/priority 字段，_entries 简化为直接存储类引用
- 修正 __init_subclass__ 避免叶子类（AutoRegressiveLM 等）创建空注册表
- 删除 5 个工厂的薄 create() 覆写，统一使用 BaseFactory.create(name, *args, **kwargs)
- 删除 3 处零调用的 available_types/available_strategies 别名死代码
- 删除零调用的 BaseModelConfig.to_file 死代码
- 将 BaseConfig.from_json/to_json 重命名为 from_file/to_file，消除与子类重复
- 移除两个 inference builder 中总是被覆写的 prompt_tokens=0

tests/data/test_preprocess_config.py

@@ -53,15 +53,15 @@ def test_to_dict_roundtrip():
     assert config2.mask == {"prompt": "mask", "response": "train"}
 
 
-def test_to_json_from_json(temp_dir):
+def test_to_file_from_file(temp_dir):
     config = PipelineConfig(
         input=InputConfig(sections=_TEXT_SECTIONS),
         mask={"text": "train"},
         mask_default="mask",
     )
     path = os.path.join(temp_dir, "config.json")
-    config.to_json(path)
-    loaded = PipelineConfig.from_json(path)
+    config.to_file(path)
+    loaded = PipelineConfig.from_file(path)
     assert loaded.input.sections == _TEXT_SECTIONS
     assert loaded.mask == {"text": "train"}
 
@@ -69,8 +69,8 @@ def test_to_json_from_json(temp_dir):
 def test_dpo_config_roundtrip(temp_dir):
     config = make_dpo_chat_config()
     path = os.path.join(temp_dir, "config.json")
-    config.to_json(path)
-    loaded = PipelineConfig.from_json(path)
+    config.to_file(path)
+    loaded = PipelineConfig.from_file(path)
     assert loaded.input.sources is not None
     assert "chosen" in loaded.input.sources
     assert "rejected" in loaded.input.sources

tests/trainer/conftest.py

@@ -65,7 +65,7 @@ def create_train_config(
 
     def scheduler_fn(optim):
         return SchedulerFactory.create(
-            optim, "cosine", warmup_steps=10, lr_decay_steps=10, min_rate=0.05
+            "cosine", optim, warmup_steps=10, lr_decay_steps=10, min_rate=0.05
         )
 
     return TrainConfig(

tests/trainer/test_callbacks.py

@@ -102,7 +102,7 @@ def test_gradient_checkpointing_trainer_integration(base_test_env, random_datase
 
     def scheduler_fn(optim):
         return SchedulerFactory.create(
-            optim, "cosine", warmup_steps=10, lr_decay_steps=10, min_rate=0.05
+            "cosine", optim, warmup_steps=10, lr_decay_steps=10, min_rate=0.05
         )
 
     train_config = TrainConfig(
@@ -136,7 +136,7 @@ def test_callback_integration(base_test_env, random_dataset):
 
     def scheduler_fn(optim):
         return SchedulerFactory.create(
-            optim, "cosine", warmup_steps=10, lr_decay_steps=10, min_rate=0.05
+            "cosine", optim, warmup_steps=10, lr_decay_steps=10, min_rate=0.05
         )
 
     train_config = TrainConfig(

tests/trainer/test_early_stopping.py

@@ -16,7 +16,7 @@ def test_early_stopping_simulation(base_test_env, early_stopping_dataset):
 
     def scheduler_fn(optim):
         return SchedulerFactory.create(
-            optim, "cosine", warmup_steps=10, lr_decay_steps=10, min_rate=0.05
+            "cosine", optim, warmup_steps=10, lr_decay_steps=10, min_rate=0.05
         )
 
     train_config = TrainConfig(

tests/trainer/test_train_strategy.py

@@ -36,8 +36,8 @@ def test_schedule_factory_random_configs():
                 min_rate = params["min_rate"]
                 lr_decay_steps = total_steps - warmup_steps
                 scheduler = SchedulerFactory.create(
-                    optimizer,
                     schedule_type,
+                    optimizer,
                     warmup_steps=warmup_steps,
                     lr_decay_steps=lr_decay_steps,
                     min_rate=min_rate,
@@ -52,8 +52,8 @@ def test_schedule_factory_random_configs():
                 t_mult = params["t_mult"]
                 min_rate = params["min_rate"]
                 scheduler = SchedulerFactory.create(
-                    optimizer,
                     schedule_type,
+                    optimizer,
                     warmup_steps=warmup_steps,
                     cycle_length=cycle_length,
                     t_mult=t_mult,
@@ -103,8 +103,8 @@ def test_schedule_factory_edge_cases():
         min_rate = params["min_rate"]
         lr_decay_steps = total_steps - warmup_steps
         scheduler = SchedulerFactory.create(
-            optimizer,
             "cosine",
+            optimizer,
             warmup_steps=warmup_steps,
             lr_decay_steps=lr_decay_steps,
             min_rate=min_rate,
@@ -129,8 +129,8 @@ def test_schedule_factory_state_persistence():
     min_rate = 0.1
     lr_decay_steps = total_steps - warmup_steps
     scheduler = SchedulerFactory.create(
-        optimizer,
         "cosine",
+        optimizer,
         warmup_steps=warmup_steps,
         lr_decay_steps=lr_decay_steps,
         min_rate=min_rate,
@@ -146,8 +146,8 @@ def test_schedule_factory_state_persistence():
 
     # Create new scheduler with same parameters
     new_scheduler = SchedulerFactory.create(
-        optimizer,
         "cosine",
+        optimizer,
         warmup_steps=warmup_steps,
         lr_decay_steps=lr_decay_steps,
         min_rate=min_rate,